GMT: Guided Mask Transformer for Leaf Instance Segmentation

• URL: http://arxiv.org/abs/2406.17109v1
• Date: Mon, 24 Jun 2024 19:52:27 GMT
• Title: GMT: Guided Mask Transformer for Leaf Instance Segmentation
• Authors: Feng Chen, Sotirios A. Tsaftaris, Mario Valerio Giuffrida,
• Abstract summary: Leaf instance segmentation is a challenging task, aiming to separate and delineate each leaf in an image of a plant. We propose Guided Mask (GMT), which contains three key components, namely Guided Positional Transformer (GPE), Guided Embedding Fusion Module (GEFM) and Guided Dynamic Positional Queries (GDPQ) The proposed GMT consistently outperforms State-of-the-Art models on three public plant datasets.
• Score: 14.458970589296554
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Leaf instance segmentation is a challenging multi-instance segmentation task, aiming to separate and delineate each leaf in an image of a plant. The delineation of each leaf is a necessary prerequisite task for several biology-related applications such as the fine-grained monitoring of plant growth, and crop yield estimation. The task is challenging because self-similarity of instances is high (similar shape and colour) and instances vary greatly in size under heavy occulusion. We believe that the key to overcoming the aforementioned challenges lies in the specific spatial patterns of leaf distribution. For example, leaves typically grow around the plant's center, with smaller leaves clustering and overlapped near this central point. In this paper, we propose a novel approach named Guided Mask Transformer (GMT), which contains three key components, namely Guided Positional Encoding (GPE), Guided Embedding Fusion Module (GEFM) and Guided Dynamic Positional Queries (GDPQ), to extend the meta-architecture of Mask2Former and incorporate with a set of harmonic guide functions. These guide functions are tailored to the pixel positions of instances and trained to separate distinct instances in an embedding space. The proposed GMT consistently outperforms State-of-the-Art models on three public plant datasets.

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